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( 10 Students )
( 10 Students )
LLMOps for Product Managers
Empower product managers to lead AI initiatives by mastering the lifecycle, operations, deployment, and monitoring of large language models.
Course Duration: 10 Hours
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LLMOps for Product Managers is a strategic, self-paced online course crafted to equip non-technical and semi-technical product leaders with the essential tools, vocabulary, and frameworks needed to manage and scale Large Language Models (LLMs) in production environments.
As AI rapidly moves from labs to live products, LLMOps—the practice of managing the operational lifecycle of LLMs—has emerged as a critical function. Product Managers are at the helm of these transformations, tasked with aligning LLM capabilities to business outcomes, user needs, compliance mandates, and engineering constraints.
This course offers a comprehensive introduction to the key components of LLMOps, demystifying technical complexity and empowering product managers to collaborate effectively with AI/ML teams. Topics span from model selection, prompt engineering, and fine-tuning to CI/CD for LLM pipelines, observability, safety, and cost management. You’ll also learn to evaluate tools like LangChain, Weights & Biases, BentoML, and vector databases such as Pinecone and FAISS.
Whether you're launching an LLM feature within a SaaS app or overseeing AI platform development, this course provides the structured knowledge to lead confidently, speak the same language as ML engineers, and make informed decisions about deploying and managing LLMs in production.
How to Use This Course
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Start with Fundamentals: Even if you're unfamiliar with AI, the early modules will bring you up to speed.
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Apply PM Thinking: Connect LLMOps topics to business value, feature delivery, and cross-functional collaboration.
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Use Frameworks and Templates: Downloadable checklists, feature specs, and RAG architecture diagrams help bridge concept to execution.
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Participate in Discussions: Share your product challenges, roadmap issues, and use case ideas with the learning community.
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Return Often: As you engage in real projects, revisit modules for decision-making support and updated tooling insights.
Course/Topic 1 - Coming Soon
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The videos for this course are being recorded freshly and should be available in a few days. Please contact info@uplatz.com to know the exact date of the release of this course.
Course Objectives Back to Top
By the end of this course, you will be able to:
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Understand the LLM lifecycle from training to deployment and feedback.
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Define LLMOps and explain its importance to business stakeholders.
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Identify the differences between fine-tuning, prompt engineering, and retrieval-augmented generation (RAG).
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Evaluate open-source vs proprietary models based on product goals.
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Collaborate effectively with data science, engineering, and legal teams on LLM integration.
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Make informed decisions about infrastructure, API usage, and model monitoring.
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Lead the creation of safe, ethical, and aligned LLM features.
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Interpret observability metrics such as latency, drift, and cost.
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Define KPIs for AI products and manage experimentation at scale.
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Oversee the end-to-end delivery of an LLM-powered product feature.
Course Syllabus Back to Top
Course Syllabus
Module 1: Introduction to LLMOps
- What is LLMOps?
- Role of PMs in LLM-powered products
- Overview of the LLM lifecycle
Module 2: Foundation Models and Product Thinking
- Open-source vs closed-source LLMs
- Choosing the right model: cost, latency, alignment
- Building vs buying considerations
Module 3: Prompt Engineering for PMs
- Prompt templates and chains
- Prompt testing and evaluation
- Case studies: Retrieval, summarization, content generation
Module 4: Fine-Tuning vs RAG
- When to fine-tune?
- Introduction to vector databases (Pinecone, FAISS)
- Architecting RAG workflows with LangChain or LlamaIndex
Module 5: Tools of the Trade
- LangChain, Weights & Biases, PromptLayer
- Model deployment tools: BentoML, Ray Serve, vLLM
- Observability & analytics platforms
Module 6: Safety, Ethics & Governance
- Hallucination prevention
- Human-in-the-loop workflows
- Responsible AI checklists for PMs
Module 7: Infrastructure and Deployment
- CI/CD for LLM pipelines
- Model versioning and rollback
- Choosing inference providers (OpenAI, AWS Bedrock, etc.)
Module 8: Evaluation & Monitoring
- Defining and tracking LLM KPIs
- Latency, drift, token cost, and quality metrics
- Creating dashboards for product review
Module 9: LLM Product Management Framework
- LLM product feature spec template
- Experimentation and A/B testing
- Stakeholder alignment & roadmap integration
Module 10: Real-World Case Studies
- LLMs in customer support, legal, education, HR, healthcare
- Failures and success patterns
- Lessons learned from scaling AI features
Module 11: Capstone Exercise
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Design a full LLMOps plan for a real or hypothetical feature
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Peer review and instructor feedback
Certification Back to Top
Learners who complete this course will receive a Certificate of Completion from Uplatz, validating their ability to lead AI-powered product development with a strong grasp of LLMOps. This credential affirms a product manager’s fluency in working with engineering, data, and compliance teams on designing, deploying, and optimizing LLM features. The certification highlights your ability to translate LLM capabilities into business outcomes while managing complexity, cost, and safety. It becomes a powerful asset in roles involving AI transformation, innovation, and platform ownership.
Career & Jobs Back to Top
As AI becomes central to software products, Product Managers with LLMOps fluency are increasingly in demand. Enterprises, startups, and SaaS companies are all racing to embed LLM features in their platforms—from chat assistants and intelligent search to AI co-pilots.
With LLMOps expertise, you can pursue roles such as:
- AI Product Manager
- LLM Product Owner
- Technical Product Manager – ML/AI
- Head of AI Product Strategy
- Platform Product Manager – GenAI
Industries adopting LLMs include:
- Healthcare (clinical assistance, summarization)
- Fintech (compliance, fraud detection)
- Legal Tech (document drafting, contract review)
- EdTech (personalized learning)
- Enterprise SaaS (AI copilots, knowledge assistants)
This course prepares you to take ownership of complex AI initiatives, contribute meaningfully in cross-functional teams, and lead product development efforts that harness the power of LLMs effectively and responsibly.
Interview Questions Back to Top
1. What is LLMOps and why is it important?
LLMOps refers to the set of practices for managing the lifecycle, deployment, monitoring, and governance of Large Language Models in production. It ensures AI systems are scalable, reliable, and aligned with business needs.
LLMOps refers to the set of practices for managing the lifecycle, deployment, monitoring, and governance of Large Language Models in production. It ensures AI systems are scalable, reliable, and aligned with business needs.
2. What’s the difference between fine-tuning and prompt engineering?
Prompt engineering tailors model behavior at inference using crafted input, while fine-tuning alters model weights by training it on new data for domain-specific understanding.
Prompt engineering tailors model behavior at inference using crafted input, while fine-tuning alters model weights by training it on new data for domain-specific understanding.
3. What is RAG and when is it useful?
Retrieval-Augmented Generation (RAG) is an architecture where the model retrieves relevant data from an external knowledge base to ground responses, reducing hallucinations and improving accuracy.
Retrieval-Augmented Generation (RAG) is an architecture where the model retrieves relevant data from an external knowledge base to ground responses, reducing hallucinations and improving accuracy.
4. What is model drift and how can it be detected?
Model drift occurs when the model’s outputs become less accurate over time due to changes in input patterns. It's detected by monitoring output quality metrics and user feedback.
Model drift occurs when the model’s outputs become less accurate over time due to changes in input patterns. It's detected by monitoring output quality metrics and user feedback.
5. How can a PM define KPIs for an LLM-powered product?
KPIs may include task success rate, latency, cost per token, user satisfaction, and rate of hallucination—aligned with the product’s goals and user experience.
KPIs may include task success rate, latency, cost per token, user satisfaction, and rate of hallucination—aligned with the product’s goals and user experience.
6. What observability tools are used for LLMs?
PromptLayer, Arize AI, and Weights & Biases help track prompt-response pairs, model versions, errors, latency, and usage patterns in production.
PromptLayer, Arize AI, and Weights & Biases help track prompt-response pairs, model versions, errors, latency, and usage patterns in production.
7. What are some safety concerns with LLMs in production?
Risks include hallucinations, biased outputs, sensitive data leakage, and prompt injection. These require mitigations like guardrails, moderation, and human review.
Risks include hallucinations, biased outputs, sensitive data leakage, and prompt injection. These require mitigations like guardrails, moderation, and human review.
8. How should a PM choose between open-source and closed models?
Evaluate factors like cost, control, customization, latency, compliance needs, and whether the model’s license fits the product’s commercial use case.
Evaluate factors like cost, control, customization, latency, compliance needs, and whether the model’s license fits the product’s commercial use case.
9. What is a CI/CD pipeline for LLMs?
It’s a set of automated steps to test, validate, and deploy changes to prompts, models, or APIs, ensuring reliable and fast iteration on LLM features.
It’s a set of automated steps to test, validate, and deploy changes to prompts, models, or APIs, ensuring reliable and fast iteration on LLM features.
10. How does a product manager work with vector databases?
While not managing databases directly, PMs should understand how vector search enables RAG, which tools are in use (e.g., Pinecone), and how relevance impacts product outcomes.
While not managing databases directly, PMs should understand how vector search enables RAG, which tools are in use (e.g., Pinecone), and how relevance impacts product outcomes.
Course Quiz Back to Top
FAQs
Back to Top
Q1. What are the payment options?
A1. We have multiple payment options:
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A2. Yes, you will receive course completion certificate from Uplatz confirming that you have completed this course with Uplatz. Once you complete your learning please submit this for to request for your certificate https://training.uplatz.com/certificate-request.php
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Q17. Does your course guarantee a job?
A17. Our course is designed to provide you with a solid foundation in the subject and equip you with valuable skills. While the course is a significant step toward your career goals, its important to note that the job market can vary, and some positions might require additional certifications or experience.
Remember that the job landscape is constantly evolving. We encourage you to continue learning and stay updated on industry trends even after completing the course. Many successful professionals combine formal education with ongoing self-improvement to excel in their careers. We are here to support you in your journey!
Q18. Do you provide placement services?
A18. While our course is designed to provide you with a comprehensive understanding of the subject, we currently do not offer placement services as part of the course package. Our main focus is on delivering high-quality education and equipping you with essential skills in this field.
However, we understand that finding job opportunities is a crucial aspect of your career journey. We recommend exploring various avenues to enhance your job search:
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b) Networking: Attend industry events, workshops, and conferences to build connections with professionals in your field. Networking can often lead to job referrals and valuable insights.
c) Online Professional Network: Leverage platforms like LinkedIn, a reputable online professional network, to explore job opportunities that resonate with your skills and interests.
d) Online Job Platforms: Investigate prominent online job platforms in your region and submit applications for suitable positions considering both your prior experience and the newly acquired knowledge. e.g in UK the major job platforms are Reed, Indeed, CV library, Total Jobs, Linkedin.
While we may not offer placement services, we are here to support you in other ways. If you have any questions about the industry, job search strategies, or interview preparation, please dont hesitate to reach out. Remember that taking an active role in your job search process can lead to valuable experiences and opportunities.
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